Early Detection of Sepsis Induced Deterioration Using Machine Learning

Sepsis is an excessive bodily reaction to an infection in the bloodstream, which causes one in five patients to deteriorate within two days after admission to the hospital. Until now, no clear tool for early detection of sepsis induced deterioration has been found. This research uses electrocardiograph (ECG), respiratory rate, and blood oxygen saturation continuous bio-signals collected from 132 patients from the University Medical Center of Groningen during the first 48 h after hospital admission. This data is examined under a range of feature extraction strategies and Machine Learning techniques as an exploratory framework to find the most promising methods for early detection of sepsis induced deterioration. The analysis includes the use of Gradient Boosting Machines, Random Forests, Linear Support Vector Machines, Multi-Layer Perceptrons, Naive Bayes Classifiers, and k-Nearest Neighbors classifiers. The most promising results were obtained using Linear Support Vector Machines trained on features extracted from single heart beats using the wavelet transform and autoregressive modelling, where the classification occurred as a majority vote of the heart beats over multiple long ECG segments